The importance and diffusion of knowledge in the agricultural sector: the Polish experiences

45The importance and diffusion of knowledge in the agricultural sector: The Polish experiences

AbstractThe main aim of the work described here was to investigate knowledge transfer to farms, as well as to analyse the levels of knowledge present as this relates to farm performance. Possible inefficiency of knowledge utilisation was investigated at the levels of the indi-vidual farm, the gmina (commune – unit of local government administration in Poland) and the region. It emerged from this that the performance of farms was closely related to level of knowledge, with results offering a basis for the elaboration of different models for knowledge transfer in agriculture. Specifically, the three models distinguished in relation to the path of information flow are peer to peer, global information or direct from supplier.

Key wordsknowledge transfer ∙ human capital ∙ rural policy ∙ rural areas ∙ rural development ∙ agricultural production ∙ Poland

The range of factors determining the economic per-formances of farms have changed over time. While natural conditions were critical historically where ag-riculture production was concerned, the later achieve-ments of agronomy and the management sciences have been such as to ensure that these aspects proved to be of ever-greater importance. Nevertheless, chang-es of this kind come at a price, since farmers must be familiar with the current state of biological, techni-cal and economic knowledge, which is to say that the manager of a contemporary farm must understand, not only the processes influencing agricultural produc-tion directly, but also aspects of the surrounding social and economic environment.

The growth of the non-agricultural functions of ru-ral areas is creating opportunities for the potential of rural areas to be exploited more efficiently. In essence, farmers’ incomes are seen to benefit where agriculture production is combined with off-farm activities (Hill & Cook 1996). Diversification of sources of income on family farms is seen to stand in the way of what would otherwise be a steadily worsening economic situation reflecting fluctuating agricultural prices and terms of trade development unfavourable for farmers.

This implies that a farm’s success may be markedly associated with skilful resource reallocation. “Skilful” is a key word here, since the effectiveness of reallocation is found to depend on the degree of innovativeness farmers show as they explore the multifunctionality of rural areas (Swagemakers 2003). This is to say that the

Geographia PolonicaVolume 85, Issue 1, pp. 45-56http://dx.doi.org./10.7163/GPol.2012.1.4

INSTITUTE OF GEOGRAPHY AND SPATIAL ORGANIZATIONPOLISH ACADEMY OF SCIENCES

http://www.igipz.pan.pl/

http://www.geographiapolonica.pl/

THE IMPORTANCE AND DIFFUSION OF KNOWLEDGEIN THE AGRICULTURAL SECTOR: THE POLISH EXPERIENCES

Zbigniew Floriańczyk1 ∙ Krzysztof Janc2 ∙ Konrad Ł. Czapiewski3

1Institute of Agricultural and Food EconomicsŚwiętokrzyska 20, 00-002 Warsaw: Polande-mail address: [email protected]

2Institute of Geography and Regional DevelopmentUniversity of WrocławKuźnicza 49/55, 50-138 Wrocław: Poland e-mail address: [email protected]

3Institute of Geography and Spatial OrganizationPolish Academy of SciencesTwarda 51/55, 00-818 Warsaw: Polande-mail address: [email protected]

Introduction

Geographia Polonica 2012, 85, 1, pp. 45-56

46 Zbigniew Floriańczyk . Krzysztof Janc . Konrad Ł. Czapiewski

growth models proposed by Lucas (1988) and Romer (1990, 1994), and in the wake of their work, growth theories came to be complemented by knowledge rep-resented in terms of the level of human capital (Chat-terji 1998; Bassanini & Scarpetta 2002; Lloyd-Ellis & Roberts 2002; Brunello & Comi 2004). Prior to that work, knowledge as part of human capital had become a fundamental descriptor of concepts ex-plaining differences in the effectiveness of econo-mies. Human capital as defined by Becker (1962) stressed the importance of investment in personal knowledge via public education, training and infor-mation on economic systems. The concept of human capital after de la Fuente and Ciccone (2002) in turn stressed the knowledge and skills accumulated by way of education, training and personal experience that is useful in the production process as well as a stimulant to the further accumulation of knowledge.

Thus knowledge in agriculture, as in all sectors of the economy, can be assumed a key factor stimulat-ing increases in productivity through the better utilisa-tion of resources. In the first half of the 20th century, Walter Wilcox pointed out that “educational work (…) is needed to acquaint farmers with the available in-formation on the productiveness of certain forms of capital in their farming systems” (Wilcox 1943: 63). At that point knowledge became an explanatory vari-able where agriculture growth was concerned. The next half-century saw developed models for agricul-tural growth emphasising the role of new technologies and the level of farmers’ knowledge, while at the same time pointing out the external origins thereof (Eicher & Staatz 1998). In fact, among the theoretical models ex-isting for agricultural growth, it is the induced innovation model proposed by Hayami and Ruttan (1985) that has technical change as endogenous.

Contemporary concepts of agriculture develop-ment base themselves to a marked degree on advanc-es in technology. In the case of European agriculture, it is the paradigm of multifunctionality (Van Huylen-broeck & Durand 2003) that dominates, stressing that the successful operation of a farm depends on a multi-disciplinary and trans-disciplinary approach to skills. This is accounted for by a changing in the fields of operation of farms from the agricultural through to the ‘rural business’-related, this entailing farmers being ‘trans-formed’ into managers. In practice, this denotes – on the one hand – a search for opportunities on the part of farm-ers, and – on the other – a monitoring of the changing en-vironment in which farms operate, this requiring continuous furthering of the level of knowledge possessed (Czapiewski & Janc 2008). This points towards an adjustment to the tra-ditional means by which farmers’ knowledge expands, which is to say primarily via the stationary educational services. Invoking the aforementioned van Huylenbroeck and Guy Durand development paradigm, we note how adjust-ment of the educational services available to farm-ers should both broaden the range of knowledge on offer and provide for more far-reaching spe-cialisation. This kind of complexity to the process of knowledge transfer is what leaves effectiveness rather sensitive to the detection of knowledge-related structures.

multifunctional rural development concept is demands more in terms of knowledge than the traditional one focusing on agriculture per se. There will need to be a combining of the knowledge as regards agronomy (allowing a farm operator to recognize and adopt the most promising farm technologies) with the knowl-edge necessary to run non-agricultural businesses. In this regard, European development strategy empha-sizes the roles to be played by knowledge and innova-tion where economic progress is concerned (European Commission 2010), at the same time recognizing that a reluctance of some parts of our societies to embrace innovation is a major obstacle to development. Over-all, the growing role for, and spectrum of, knowledge regarding the agricultural and rural development process is sufficient to justify a reinvestigation of how knowledge affecting the performance of farms as multifunctional rural enterprises.

Specifically, the analysis presented combines mi-croeconomic data on farms with aggregated indicator data for EU regions and Polish gminas with a view to describing spatial aspects to the role level of knowl-edge plays in agriculture and rural development in the first decade of the 21st century. This is done in order for knowledge transfer to farms to be investigated, as well as knowledge level and relationships with farm performance analysed. Conclusions are arrived at so that these might be used in elaborating different knowledge-transfer models in relation to agriculture effectiveness and the support process where the plan-ning of rural development policies is concerned.

Knowledge in agricultural and rural development concepts

The beginning of the 20th century brought intensified discussion on knowledge and its place in social and economic progress. The basic question was related to the nature of knowledge, as represented by technology and human skills that were recognized to be external in relation to capital (Clark 1907). Contrary to this ear-ly concept of the role of knowledge in progress, Veblen (1908a, b) argued that knowledge is the most impor-tant factor of production, i.e. relevant to the creation and utilisation of material capital. In other words, the value of material capital can be taken to be defined by knowledge of the people who use it. This concept was developed and applied in agriculture by Schultz (1961), who recognized knowledge as an integral part of all-inclusive productive capital. This allowed for ac-curate valuation of human capital’s role in agricultural growth, as this corresponds with other elements to the capital deployed in the name of production. Despite this pioneering work, until the 1980s, technological change in production was recognised as exogenous by the prevailing group of economists (McCormick 2002). This concept was based on the neoclassical model of economic grow proposed by Solow (1956), which in-tegrates technological change as a multiplier where the production function is concerned. Changes in pro-duction technology were treated as endogenous to the



al knowledge more appropriate for a multifunctional type of development.

The complex structures of rural knowledge required offer an indication that modern information and com-munications technologies (ICT) constitute a proper measure where challenges of knowledge transfer are concerned. The ICT potential as regards knowledge diffusion is of particular interest in agriculture, which is by definition spatially dispersed, with all the rela-tively unfavourable consequences that can have for both creativity and knowledge transfer. Among other things, ICT can effectively shrink the physical distance separating rural areas from metropolises (knowledge centres)-(Johnson 2001; Malecki 2003). Indeed, it might be stressed that a immanent characteristic of ICT development is its urban origin (Poncet & Ripert 2007). This implies uneven access to, and uneven uti-lization of, ICT, between urban and rural areas (Fox & Porca 2007; Whitacre & Mills 2007). In extremis, higher costs associated with infrastructural develop-ment combine with a situation in which the rural popu-lace is clearly less skilled with the utilization of modern ICT, to create an environment for a digitally divided (ru-ral versus urban) society (Grimes 2000; Warren 2007).

This means that uneven chances for development when rural populations are set against urban popu-lation can be characterized in relation to relatively poorer access to knowledge and a more limited abil-ity to utilise modern communications technologies. Similarly, agriculture’s traditionally strong dependence on local natural and social resources creates natural internal forces for stabilisation. These are a key prob-lem where multi-directional rural development is pos-tulated. The problem of adequate knowledge transfer into European agriculture and rural areas is seen to be more serious in regions with a large number of subsist-ence farms, as subsistence-type farms prefer informal types of knowledge that relate to succession and own experience. In contrast, modern agriculture is largely founded upon links with formal knowledge based on research achievements (Klepacki 2005). The growing complexity of the knowledge-transfer issue in rural areas can therefore be characterized as a problem of proper transfer and targeting.

Interdependence between knowledge and farm performance – the Polish case

In most countries, farmers belongs to a socio-eco-nomic group with a below-average level of education. As of 2006, over half of all inhabitants of Poland’s rural areas working in non-agricultural sectors of the econo-my had secondary or tertiary education, as compared with only ¼ of farmers in agriculture as such. Never-theless, the last few decades have brought a consider-able improvement in the farming population’s level of education. While in 1960, almost 97% of the farming population could at best claim to have primary educa-tion; by 2007 the comparable figure was just 26% of those in farming (Fig. 1). The change has thus been a rapid one, which has coincided with Poland’s period of systemic transformation.

The Lundvall (1996) concept for the creation and transfer of knowledge makes reference to the four major knowledge domains of ‘know-what, know-why, know-how and know-who’. While the first two of these are related to the codified knowledge accessible via such common sources as books and classes, the two last link up with the tacit knowledge possessed by in-dividuals thanks to their personal experience with solv-ing problems and their social interactions. Contempo-rary research stresses how importance it may be for a balance to be set between these codified and tacit sources and aspects of knowledge (Guile 2001). Some go yet further, suggesting that the latter (tacit) aspect be given priority (Gertler 2003; Howells 2002). What is crucial here is the degree to which tacit knowledge can be regarded as territory-related, since the unique characteristics of regions often go hand in hand with specific resources defining their potential as regards the creation and absorption of knowledge (Bathelt et al. 2004; Storper & Venables 2004). According to Hilpert (2006), such regional knowledge comes from different sources as science, tradition, culture or re-gional management. The other aspect to knowledge development relates to proximity to potential sources, as defined, not only by reference to physical space, but also in regard to the mobility of human capital, the densities of social networks and the quality of culture (Boschma & Lambooy 1999). From among a proposed five types of proximity, Boschma (2005) underlines cognitive and organizational knowledge – a type that Lagendijk and Lorentzen (2007) see as of special importance where flow between the centre and the periphery is concerned. The role of common knowl-edge as a part of the environment necessary for the achievement of personal success is stressed by Florida (2004) and Törnqvist (2004), while other researchers feel that a proper milieu reflecting common knowl-edge is what stimulates the creativity critical for an innovative economy milieu (Gössling & Rutten 2007). The structural problem of the new knowledge transfer and its implementation is further complicated in rural areas by agriculture diversity (Floriańczyk et al. 2009). While conventional farms demand highly-specialised technologies, those in transition are rather in need of knowledge relating to non-agricultural opportunities.

In practice, these two types of knowledge essential for modern agriculture and rural development are of interest to development policies. Thus the EU’s Com-mon Agricultural Policy offers training programs for farmers, as well as programs that assist directly with the introduction of modern farming technologies (Eu-ropean Commision 2009). However, since a general trend for possibilities for employment in non-agricultur-al sectors to grow can lead to a withdrawal of human capital from farms that can not provide satisfactory incomes, it may be the case that knowledge intended to stimulate agricultural development actually ends up supporting other sectors of the economy. This phenom-enon can be described as a 2-layer inefficiency, where-in the first layer reflects unnecessary public spending on a knowledge increase that is only taken advantage of in part, while the second relates to a failure to en-sure that rural areas are provided with non-agricultur-



with agricultural education is to be observed – farm-ers with specialised (agricultural) education operate larger farms than those with the same level of non-agricultural education. On the other hand, a higher level of non-agricultural education is accompanied by relatively higher incomes from activities outside agri-culture. However, this is only true as far as average values are concerned. In the case of the top 5% of incomes (specifying the minimal level of income of the best-performing 5% of farms), agricultural education prevails over non-agricultural as regards both kinds of activity.

The gmina (local-authority) level of investigation

The second level of investigation compares advances in farmers’ agricultural education with the value of farm market production. The level of productivity of private agriculture was defined as the value of pro-duction sold on the market, as expressed in Polish zloty (PLN) per hectare of farm land (Kulikowski 2003).

Recently about 70% of agricultural production in Poland has been market-orientated. However the level of market production differs between farms, and de-pends mainly on farm size and production profile. The assessment of the spatial interrelationship between human capital and farm performance seeks to present the geographical aspect to the creation and utilization of agricultural knowledge. Specifically, the identifica-tion of territories with a high level of knowledge, fa-vourable from the point of view of social structure, is assumed to be critical where these can be potential

Possible inefficiency of knowledge utilisation is here investigated on three levels, of which (national) brings together national statistics, plus accountancy data col-lected for Polish farms under the Farm Accountancy Data System (FADN)1. The latter data represent the farms that are core producers of agricultural goods, some 12,000 farms in Poland’s case (as set against 750,000 farms overall).

The second level of investigation centres on aggre-gated statistical data for Poland’s gminas (LAU2 level), while the third reflects data for the EU’s NUTS3 sub-regions. In all cases, analysis involves the relationship between indicators describing levels of education of farm managers, the value of output, and incomes from non-agricultural activities. The results at these dif-ferent levels of investigation provide for methodologi-cal triangulation (Yeung 1997).

The farm level of investigation

The analysis of microeconomic interdependence be-tween the levels of knowledge possessed by farmers and the economic performance of their farms attests to interdependence between the level of education of a farm operator and the economic size of the farm (Tab. 1). Farms operated by a person with a higher lev-el education are characterised by the highest incomes both from farming and from non-agricultural activity. When it comes to farm sizes, a strong relationship

1 The Farm Accountancy Data Network (FADN) is a system obliging Member States to apply uniform methodology in the collection of structural and accountancy data on farms. For more information see http://ec.europa.eu/agriculture/rica/.

Figure 1. Breakdown of Poland’s farming population by level of education 1960-2007. Source: calculations after Maciejewski (2005) and Central Statistical Office (Farm Structure Survey 2007).



sources of transfer. In the categorisation that has been carried out, such regions with favourable prospects when it comes to the development of the agricul-tural function are identified as class I and are seen to account for some 31% of all gminas. These are mainly located in the voivodships: Wielkopolskie, Kujawsko-Pomorskie, Pomorskie (mostly the Vistula Delta – Żuławy region), the northern part of Łódzkie and the north-western part of Mazowieckie (Fig. 2). These rural areas have prospects of making their development dependent on a market-oriented agri-cultural function. Standing in contrast are the 42% of gminas (in class IV) that will have to find other development pathways, because both of the investi-gated characteristics (endogenous potential expressed in terms of educational level and the productivity of agriculture) were at below-average values. What is in-teresting is that there are a few gminas assigned to the transitory classes (classes II and III – 17%). This analysis points to the regionalisation of knowledge-transfer demand as regarding the multifunctional

development of rural areas. A preponderance of the gminas with poorly-performing agriculture and a low level of knowledge are located in the south-east of Po-land. These could be characterised as displaying the greatest demand for rural knowledge, while being poorly endowed where agricultural development is concerned. On the other hand, gminas in the centre of Poland enjoy the greatest potential to specialise fur-ther in agricultural production.

Level of investigation involving the EU regions

The third-level analysis conducted for the EU (NUTS2 level) regions reveals a strong correlation (r=0.69) between the level of education of farmers and the economic size of farms (ESU; Fig. 3). Polish regions as compared with EU ones can be characterised by the average level of farmer education and lower eco-nomic size of farms. This suggests that human capi-tal in Polish agriculture is less effective than expected in terms of transferring it into farm development. In-deed, quantified model of transformation knowledge at EU level proves the unsatisfactory effectiveness of Polish farmers. According to the regression model Polish farmers could operate farms that are almost 10 times larger than observed (39 ESU instead of 4 ESU). Assuming that the relation between analysed data is non linear, Polish farms are characterised as being five times smaller than expected considering av-erage economic size (average of expected value from

the model 22 ESU). The most important factor, that shape such a results is incompatible agrarian struc-ture in Polish agriculture – average farm area is only 6.6 ha to compare with the 11.5 ha for the whole EU and 20.2 ha for EU-15 countries.

The study shows a high potential for the multifunc-tional development of Polish rural regions. A higher level of education accompanied by limited economic potential of farms suggests that the economic

Table 1. Interdependence between the level of education of those running farms and economic performance.

Level of education

Farm size in European Size Unit (esu)

Incomes from non-agricultural activities [Pln]

Farm income [Pln]

ā Me top 5% ā top 5% ā Me top 5%

Primary 16.4 11.0 46.8 2,891 15,600 59,683 35,119 182,976

Vocational non-agricultural

15.6 10.7 44.1 3,657 19,250 56,749 35,052 181,133

Vocational agricultural

21.9 15.8 60.6 2,474 15,600 74,172 48,441 224,716

Secondary non-agricultural

18.3 10.8 56.8 5,043 25,000 77,044 38,686 245,559

Secondary agricultural

24.4 16.1 66.3 3,607 20,700 90,297 54,124 281,316

Tertiary non-agricultural

20.0 10.5 74.6 8,595 15,600 88,320 38,236 390,976

Tertiary agricultural 30.1 14.6 94.8 8,448 39,000 130,063 54,324 438,642

Signatures: ā – arithmetic mean; Me – median; top 5% – minimal level of income of the higher-performing 5% of farms (also maximal level of income of the lower-performing 95% farms).Source: calculated on the FADN data for 2006.



Knowledge transfer to rural areas as understood traditionally is concerned with modern agricultural technologies. The induced innovation model of ag-riculture development in particular links technical changes with agriculture progress assisted by insti-tutional innovation (Hayami & Ruttan 2006). In this model, progress needs to overcome constraints on the expansion of production arising out of scarcity of supply of production resources. Internally-driven willingness to change is accompanied by interac-tions between farmers and the world of science responding with new, demanded technologies. The induced innovation model therefore underlines a sensitivity on the part of scientific and public insti-tutions when it comes to the extension of support to agricultural development. Following the new para-digm of rural development (OECD 2006), the in-duced innovation development model is extended to cover a multisectoral concept for rural areas; hence this article’s references to knowledge transfers into both agricultural and non-agricultural activities.

The authors thus propose three models of knowl-edge transfer that are to be distinguished in line with the path of information flow, i.e. peer-to-peer, global

situation of rural areas in Poland could be improved significantly were more rural knowledge resources to be utilised outside agriculture. Interdependencies be-tween level of farmer education and farm production and incomes performance characterize the ability of operator to accordingly increase the knowledge and develop farm. This process is strongly related to farm-ers skill to gather and utilize information from market and readiness to implement innovations.

Modelling the transfer of agriculture knowledge

The distinct relationship between level of knowledge and the performance of farms presented above calls for an analysis of potential channels by which knowl-edge transfers on to farms. Account is taken here of the relatively easier transfer of codified – as opposed to tacit – knowledge. Equally, as the codification of knowledge is a basis and a starting point for the ac-quisition of tacit knowledge, it is assumed that the transfer of knowledge in codified form is proportionally followed by the transfer of tacit knowledge.

Figure 2. Spatial typology of gminas by level of the agricultural education of farmers (A) and the level of agricultural productivity (B). Identities of the Polish regions (voivodships): 1 – Zachodniopomorskie; 2 – Pomorskie; 3 – Warmińsko-Mazurskie; 4 – Podlaskie; 5 – Kujawsko-Pomorskie; 6 – Lubuskie; 7 – Wielkopolskie; 8 – Łódzkie; 9 – Mazowieckie; 10 – Lubelskie; 11 – Dolnośląskie; 12 – Opolskie; 13 – Śląskie; 14 – Świętokrzyskie; 15 – Małopolskie; 16 – Podkarpackie.Source: based on the Central Statistical Office data.



ing of farm competitiveness and incomes), as well as support for rural sustainable development as broadly conceived. In this light, the extension ser-vices system in Poland is potentially in line with the new paradigm of rural development, i.a. by covering activities beyond agricultural production. However, the preferences for top-down information flow could distort the balance between formal knowledge that is of external origin. Furthermore, it may become an obstacle to the utilisation of endogenous develop-ment factors that are locally specific.

Field research points to a strong correlation be-tween a farmer’s educational level and the degree to which advisory services are utilized2. 30% of all respondents declared that they had taken advan-tage of extension services in farm management. Within that group, the most frequent users of exten-sion services are farmers between 40 and 50 years old. Following them is a group of younger farmers aged 30-40. Farmers of other ages seldom seek support from extension services. As regards level of education, the differences between farmers that have achieved tertiary or secondary levels do not achieve statistical significance. However, farmers in

2 Data taken from the Agrinpol Project database for the years 1999-2002 and 2004-2006 (Agrinpol 2006).

information and direct-from-the-supplier (Floriańczyk 2007). In the peer-to-peer model, a farmer pos-sesses an amount of knowledge received directly from an adviser or another farmer sufficient to im-plement new technology without further assistance. The model of global information in turn bases itself around general information regarding new technol-ogy being supplied by public-access media. In a sec-ond step, detailed information is to be collected from an adviser or another farmer. As in the peer-to-peer model, the last stage here is the implementa-tion of change. Finally, the direct-from-the-supplier model corresponds to straight knowledge transfer from the provider of it to the farmer. By way of this process, farmers seek possibilities for expansion on their farms through direct contact with a potential supplier of knowledge.

The peer-to-peer model

Extension services officially responsible for training, advisory and agricultural consultancy were forced to restructure their organisational structures and scope of operations. The conferment of knowledge by the Agricultural Advisory Centre has as its aim the improvement of agricultural performance (the rais-

Figure 3. Average economic size of farm in ESU (logarithmic scale) against level of agricultural education of those running farms (in 2005).Source: calculations based on data of the DG AGRI (2009).



formation for the farm manager that can be verified with the support of extension services, at the second stage of knowledge transfer. Positive assessment of the information results in the application of the new production technology or management. The global in-formation model for knowledge transfer with the use of the Internet, after Warren (2002, 2004), tends to be used intensively on larger farms. Farms with greater resources make major use of computers in manage-ment, and utilize the Internet to gather information, not only on market development, but also on new technologies of production. These findings are in sharp contrast with similar investigations carried out in Po-land’s Małopolskie voivodship. The results of research done by Cupiał and Szeląg-Sikora (2008) point to only marginal use of the Internet on farms, be these small or large. This phenomenon can be accounted for by limited confidence in the quality of information avail-able through such open-access resources as the Inter-net.

However, the higher the level of educational attain-ment of the farm operator, the more positive the at-titude when it comes to acceptance of the Internet as a source of information. Further findings of the same researchers also suggest a greater willingness of larger farms to base their technological development on vari-ous different sources of knowledge. Cross-examination here confirms the value of information and the com-petency of its sources. This creates an environment in

these two groups combined were twice as likely as those with primary education only to declare that they had had contacts with extension services (Tab. 2).

Correlation between frequency of extension servic-es utilisation and level of education is to be observed regarding the level of farmer household’s standards and perceiving the positive outcomes of accession to the EU (Bański 2005). Within the investigated group about 30% of farmers declare frequent collaboration with extension services. On the other hand the share of non-farmer rural population that contacts with exten-sion services does not exceed 10%. That reflects both the rural population opinion of agricultural speciality of extension services and improper model of knowl-edge transfer to rural population.

Global information model

The global information model of knowledge transfer to agriculture can be recognized as the one potentially offering the most promise when it comes to the ap-plication of modern communications technologies. Information on new technologies of production and management, as well examples of their application in agriculture can be disseminated with the aid of televi-sion, specialized newspapers and the Internet, these all guaranteeing wide-ranging distribution. The Inter-net in particular has a potential to provide specific in-

Figure 4. Three models for knowledge transfer in agriculture.Source: based on Floriańczyk (2007).

Table 2. Farmers’ collaboration with extension services.

Educational level Collaboration with extension services Population (statistic sample)

Tertiary 38 [%] 29

Secondary 36 [%] 290

Vocational 30 [%] 443

Primary 18 [%] 169

Total 30 [%] 931

Source: calculations based on Bański (2005).



which broad use may be made of the Internet in rural areas of Poland, but still shows the gap between ICT utilization in farming as characterizing agriculture in the East and West. Limited professional utilization of the Internet on Polish farms is accompanied by a rela-tively higher level of utilization of computers for other purposes. According to Czapiński and Panek (2009) and GUS data (2008), some 30% of farmers use computers, while – depending on the source of data – 25-28% utilize the Internet. This can be considered a significant improvement, since in 2005 only 10% of farmers declared that they utilized the Internet. On the other hand, only the socio-economic group comprising the retired and pensioners were less familiar with the utilization of this technology. Empirical evidence from both aforementioned investigations reveals significant differences between rural and urban areas when it comes to role the Internet plays in the global informa-tion model for knowledge transfer. The less-favourable position of the rural population is to be explained by differences in accessibility, and in ability to use online resources. Where the effectiveness of the global infor-mation model is concerned, rural infrastructure and the human capital of the rural population are deemed to be critical factors (Fig. 4).

The direct-from-the-supplier model

The third model for knowledge transfer – direct-from- -the-supplier – omits the stage of information gathering entailing an advisor or other farmers. General infor-mation from public sources stimulates direct contact between a farmer and the supplier of technology, with a view to the latter being implemented. In this model, the majority of farms are already advanced technolog-ically, and operate on a scale relatively large enough to suggest a potential to become a direct partner for a supplier of technology. This model can be character-ised as exclusive and reserved for the high-knowledge farm operator able to recognise the opportunities of-fered by new technology, and to consider risk-taking over the investment on the basis of own observations. A further critical point in the direct-from-the-supplier model is a capacity on the part of those producing technology to attract their potential customers and provide basic information for them. The investigation conducted on farms that decided to take up new tech-nology showed that 32% of them made direct contact with the supplier of the technology with a view to ob-taining it3. This relatively large share should be set against the superior position of the farms investigated. However, the results support the hypothesis that there is high endogenous potential to develop their busi-ness on the part of farms with fuller knowledge. At the same time, the same group of respondents com-plained about the quality of extension services – 79% mentioning a knowledge gap, and a lack of informa-

3 Data taken from the Agrinpol Project database for the years1999-2002 and 2004-2006 (Agrinpol 2006).

tion regarding the possibilities for capital support to be obtained, and 56% referring to marketing skills. These findings support a conclusion regarding limited levels of specialization of extension services, as well as unsatisfactory identification of top-performance farm-ers demand, in the sense of the induced innovation model of agricultural development.

The direct-from-the-supplier model for knowledge transfer can be characterized as dependent on public institutions, such as extension services, to only a lim-ited degree. It can thus be regarded as most effective from the public finance point of view. In contrast, the extension services are directly involved in the knowl-edge transfer taking place by virtue of the peer-to-peer and global information models, these two being preferred by the small and medium-sized farms that constitute the majority. This points to increased effi-ciency of the public system with a growing number of farms. The generally limited educational potential of farming populations indicates that the direct from the suppliers model of knowledge transfer only applies to a narrow population. On the other hand, the models with advisory participation are seen to be of greater importance.

Conclusions

The classical problem of the transfer of knowledge into agriculture has become more complex in recent times. This is related with the general process by which knowledge is assuming ever greater impor-tance where economic development is concerned. A changing economic environment increases the need for specialisation and the use of up-to-date technology. In respect of agriculture, the imple-mentation of modern technological solutions used to be further complicated by the marked degree to which traditional agriculture depends on local (tacit) knowledge, and the distances separating villages from knowledge centres. Simultaneously, the capac-ity of farming activity to provide satisfactory income for an agricultural population has declined in rela-tive terms, this reflecting the faster development of other sectors of the economy. New paradigms of ru-ral development underline the multifunctionality of rural areas, indicating possibilities for rural develop-ment by way of the fuller utilisation of local resourc-es in activities beyond agriculture. Implementation of the concept of multifunctional rural development is linked with the evolution of farmers into manag-ers. This process creates a demand for knowledge of the new dimensions away from agriculture.

Among the existing theories, it is the induced innovation model of agricultural growth that is capable of addressing new rural challenges. This recognises the issue of local knowledge and defines the environment necessary for effective institutional support of the development process. Institutions as extension services are expected to monitor local de-mand for knowledge, and to make an effort to de-



liver it. New information and communications tech-nologies have the potential to facilitate the process of knowledge diffusion. However, relatively poor access and (lack of) skills among the rural popula-tion are major obstacles to these technologies being utilised.

The matter of knowledge transfer, as interrelated with the multifunctional development of rural areas concept, is of great importance in agriculture domi-nated by small family farms. Limited possibilities for agricultural output to increase entail development being directed towards the non-agriculture utilisa-tion of rural resources. Analysis of the interactions between level of knowledge and the performance of Polish farms attests to the dynamic improvement in the educational attainments of the rural popula-tion over the last few years. Farm operators with a higher level of agricultural education tend to run larger farms with higher incomes, whether these de-rive directly from farming or from non-agricultural activity. In turn, the analysis of data at gmina level clearly points to the south-east as characterised by a low level of knowledge and attendant farm perfor-mances that demand institutional support for the transfer of knowledge. On the other hand, there is also the central and western part of Poland, with its highly-educated farmers and effectively performing farms that do enjoy the potential for further devel-opment. The relationship between operators’ levels of education and farm size at regional level still suggest more limited utilisation of human capital in Polish rural areas than is the case in most other EU countries. Such ineffectiveness calls for intensified implementation of the multifunctional rural devel-opment concept. This to be connected directly with more effective application of rural human capital where activity outside agriculture is concerned.

Extension services are traditionally recognised as a main channel for the provision of agricultur-al knowledge. However, the multifunctional rural development concept requires a reorientation of their services portfolio, and regional specialisation. Among the investigated models of knowledge trans-fer to agriculture the peer-to-peer and global infor-mation models incorporate extension services. Peer--to-peer is recognised as being sensitive to the tacit kind of knowledge. Under this model, extension ser-vices respond to local-community needs with a for-mal kind of knowledge. Where the effective transfer of knowledge in this model is concerned, the target group comprises farms of low profitability and low levels of educational attainment on the part of oper-ators. The relatively poor communication achieved by this group of farmers can be explained under the induced innovation rural development model. Traditional preferences for extension services to fol-low the ‘top-down’ central planning formula do not respond to local demand, the result being limited interest in using, and in receiving assessments from, rural population extension services. South-eastern Poland is dominated by poor farms, and hence des-ignated for the application of peer-to-peer knowl-edge transfer. However, in this case a broadening

of extension-service activities is critical, for the rare contacts between the non-farmer rural population and extension services indicate that a reorientation of their activities towards the multifunctional devel-opment of rural areas is warranted.

The global information model appears to be of the most limited effectiveness when it comes to the trans-fer of knowledge to rural areas. In this model, only general information can be transferred, while modern information and communications technologies are either inaccessible to farmers or else an unreliable source of information. However, with growing educa-tional attainments on the part of the rural population, this might became an effective way of initiating the process of formal knowledge transfer.

The most effective knowledge transfer using the di-rect-from-the-supplier model is more limiting in agricul-ture due to a high initial knowledge level obstacle. This leaves it attractive to only a narrow group of farmers who possess the skills to cooperate directly with exter-nal formal-knowledge resources. Within this group of farmers, extension services are of minor importance, since they are incapable of providing the most up-to-date knowledge. In this case, extension services could rather focus on providing information on potential demand to technology suppliers, rather than farmers.

This research outcomes should be used directly in the programming of policies aiming at improvement of human capital and rural economy. It allows for precise formulation of policy instruments in order to increase its efficiency and finally insure sustainability of rural development. It should be emphasised that knowledge in any dimension (spatial and microeconomic) is cru-cial in development of rural economy. But, knowledge that is practically utilised is a characteristic of ‘a few’ farmers.

Acknowledgements Publication prepared within the project 2011/01/D/HS4/03295 “Models of knowledge transfer in agri-culture and its influence on agricultural productiv-ity – spatial analysis” financed by the Polish National Science Centre and carried out in the Institute of Geo- graphy and Spatial Organization, Polish Academy of Sciences in the years 2011-2015.



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© Zbigniew Floriańczyk, Krzysztof Janc, Konrad Ł. Czapiewski© Geographia Polonica© Institute of Geography and Spatial Organization, Polish Academy of Sciences, Warsaw, 2012

Article first received ∙ March 2012Article accepted ∙ July 2012

The importance and diffusion of knowledge in the agricultural sector: the Polish experiences

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